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Exploring User Concerns about Disclosing Location and Emotion Information in Group Recommendations

Published: 29 August 2021 Publication History

Abstract

Recent research has shown that explanations serve as an important means to increase transparency in group recommendations while also increasing users' privacy concerns. However, it is currently unclear what personal and contextual factors affect users' privacy concerns about various types of personal information. This paper studies the effect of users' personality traits and preference scenarios ---having a majority or minority preference--- on their privacy concerns regarding location and emotion information. To create natural scenarios of group decision-making where users can control the amount of information disclosed, we develop TouryBot, a chat-bot agent that generates natural language explanations to help group members explain their arguments for suggestions to the group in the tourism domain. We conducted a user study in which we instructed 541 participants to convince the group to either visit or skip a recommended place. Our results show that users generally have a larger concern regarding the disclosure of emotion compared to location information. However, we found no evidence that personality traits or preference scenarios affect privacy concerns in our task. Further analyses revealed that task design (i.e., the pressure on users to convince the group) had an effect on participants' emotion-related privacy concerns. Our study also highlights the utility of providing users with the option of partial disclosure of personal information, which appeared to be popular among the participants.

Supplementary Material

MP4 File (HT21-ht032.mp4)
Hi, my name is Shabnam Najafian last year Ph.D. candidate at TU Delft working under the supervision of professor Nava Tintarev, and today, I?m presenting the Hypertext 2021 paper, Exploring User Concerns about Disclosing Location and Emotion Information in Group Recommendations. In this paper, we looked at people's privacy decisions through their actual behavior rather than only their attitude which is the case in most privacy-related research.

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Cited By

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  • (2024)To Share or Not to Share: Understanding and Modeling Individual Disclosure Preferences in Recommender Systems for the WorkplaceProceedings of the ACM on Human-Computer Interaction10.1145/36330748:GROUP(1-28)Online publication date: 21-Feb-2024
  • (2024)KGR: A Kernel-Mapping Based Group Recommender System Using Trust RelationsNeural Processing Letters10.1007/s11063-024-11639-456:4Online publication date: 19-Jun-2024
  • (2023)How do people make decisions in disclosing personal information in tourism group recommendations in competitive versus cooperative conditions?User Modeling and User-Adapted Interaction10.1007/s11257-023-09375-w34:3(549-581)Online publication date: 12-Jul-2023
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cover image ACM Conferences
HT '21: Proceedings of the 32nd ACM Conference on Hypertext and Social Media
August 2021
306 pages
ISBN:9781450385510
DOI:10.1145/3465336
  • General Chair:
  • Owen Conlan,
  • Program Chair:
  • Eelco Herder
This work is licensed under a Creative Commons Attribution International 4.0 License.

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Association for Computing Machinery

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Publication History

Published: 29 August 2021

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Author Tags

  1. explanation
  2. group recommendation
  3. information privacy
  4. privacy concern

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HT '21
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HT '21: 32nd ACM Conference on Hypertext and Social Media
August 30 - September 2, 2021
Virtual Event, USA

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View all
  • (2024)To Share or Not to Share: Understanding and Modeling Individual Disclosure Preferences in Recommender Systems for the WorkplaceProceedings of the ACM on Human-Computer Interaction10.1145/36330748:GROUP(1-28)Online publication date: 21-Feb-2024
  • (2024)KGR: A Kernel-Mapping Based Group Recommender System Using Trust RelationsNeural Processing Letters10.1007/s11063-024-11639-456:4Online publication date: 19-Jun-2024
  • (2023)How do people make decisions in disclosing personal information in tourism group recommendations in competitive versus cooperative conditions?User Modeling and User-Adapted Interaction10.1007/s11257-023-09375-w34:3(549-581)Online publication date: 12-Jul-2023
  • (2023)Evaluating explainable social choice-based aggregation strategies for group recommendationUser Modeling and User-Adapted Interaction10.1007/s11257-023-09363-034:1(1-58)Online publication date: 21-Jun-2023

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